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Clustering, classification and explanatory rules from harmonic monitoring data

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Abstract


  • A method based on the successful AutoClass (Cheeseman & Stutz, 1996) and the Snob

    research programs (Wallace & Dowe, 1994); (Baxter & Wallace, 1996) has been chosen for

    our research work on harmonic classification. The method utilizes mixture models

    (McLachlan, 1992) as a representation of the formulated clusters. This research is principally

    based on the formation of such mixture models (typically based on Gaussian distributions)

    through a Minimum Message Length (MML) encoding scheme (Wallace & Boulton, 1968).

    During the formation of such mixture models the various derivative tools (algorithms) allow for the automated selection of the number of clusters and for the calculation of means,

    variances and relative abundance of the member clusters. In this work a novel technique has

    been developed using the MML method to determine the optimum number of clusters (or

    mixture model size) during the clustering process. Once the optimum model size is

    determined, a supervised learning algorithm is employed to identify the essential features of

    each member cluster, and to further utilize these in predicting which ideal clusters any new

    observed data may best described by.

    This chapter first describes the design and implementation of the harmonic monitoring

    program and the data obtained. Results from the harmonic monitoring program using both

    unsupervised and supervised learning techniques are then analyzed and discussed.

Publication Date


  • 2009

Citation


  • Asheibi, A., Stirling, D. A., Soetanto, D. & Robinson, D. A. 2009, 'Clustering, classification and explanatory rules from harmonic monitoring data', in E. Meng Joo & Y. Zhou (eds), Theory and Novel Applications of Machine Learning, In-Tech, Vienna. pp. 45-68.

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2239&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/1223

Book Title


  • Theory and Novel Applications of Machine Learning

Start Page


  • 45

End Page


  • 68

Abstract


  • A method based on the successful AutoClass (Cheeseman & Stutz, 1996) and the Snob

    research programs (Wallace & Dowe, 1994); (Baxter & Wallace, 1996) has been chosen for

    our research work on harmonic classification. The method utilizes mixture models

    (McLachlan, 1992) as a representation of the formulated clusters. This research is principally

    based on the formation of such mixture models (typically based on Gaussian distributions)

    through a Minimum Message Length (MML) encoding scheme (Wallace & Boulton, 1968).

    During the formation of such mixture models the various derivative tools (algorithms) allow for the automated selection of the number of clusters and for the calculation of means,

    variances and relative abundance of the member clusters. In this work a novel technique has

    been developed using the MML method to determine the optimum number of clusters (or

    mixture model size) during the clustering process. Once the optimum model size is

    determined, a supervised learning algorithm is employed to identify the essential features of

    each member cluster, and to further utilize these in predicting which ideal clusters any new

    observed data may best described by.

    This chapter first describes the design and implementation of the harmonic monitoring

    program and the data obtained. Results from the harmonic monitoring program using both

    unsupervised and supervised learning techniques are then analyzed and discussed.

Publication Date


  • 2009

Citation


  • Asheibi, A., Stirling, D. A., Soetanto, D. & Robinson, D. A. 2009, 'Clustering, classification and explanatory rules from harmonic monitoring data', in E. Meng Joo & Y. Zhou (eds), Theory and Novel Applications of Machine Learning, In-Tech, Vienna. pp. 45-68.

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2239&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/1223

Book Title


  • Theory and Novel Applications of Machine Learning

Start Page


  • 45

End Page


  • 68